Objectives

  • Deal with overplotting
  • Order bar chart
  • Zooming
  • Change labels, themes, and scales

1. Reading in data

In this section we’ll continue using CRC dataset.

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)

CRC <- read.csv("./data/CRC_train.csv")
NCI60 <- read.csv("./data/NCI60.csv")

2. Improve figure clarity

2.1 Deal with overplotting

Recall the basic scatter plot we made before

# Basic scatter plot
p <- ggplot(CRC, aes(x = SERPINA3, y = TIMP1))
p + geom_point()

geom_jitter() adds a small amount of random variation to the location of each point, and is a useful way of handling overplotting caused by discreteness in smaller datasets

# moves each point by a small, random amount
p + geom_jitter(width = 0.25)

But geom_jitter() doesn’t work well for larger datasets

s <- ggplot(NCI60, aes(BR_BT549_a, BR_HS578T_a))
s + geom_point()

s + geom_jitter()

We can change the shape from solid to hollow circles

s + geom_point(shape = 1)

Or change the point to pixel size

s + geom_point(shape = ".")

We can also use alpha blending (transparency) to make the points transparent. If we specify alpha as a ratio, the denominator gives the number of points that must be overplotted to give a solid colour.

s + geom_point(alpha = 1 / 3)

s + geom_point(alpha = 1 / 5)

s + geom_point(alpha = 1 / 10)

2.2 Order bar chart

First, we have to calculate the height of each bar manually and sort the bar height. Then make bar plot with stat="identity"

g <- ggplot(CRC, aes(Sub_group)) 
g + geom_bar()

subgroup <- CRC %>% group_by(Sub_group) %>% summarise(n = n()) # count the number of samples for each sub group
subgroup
## # A tibble: 3 × 2
##   Sub_group     n
##      <fctr> <int>
## 1    Benign    34
## 2       CRC   100
## 3   Healthy    66
subgroup <- subgroup[order(subgroup$n), ]  # sort
subgroup$Sub_group <- factor(subgroup$Sub_group, levels = subgroup$Sub_group)  # to retain the order in plot.
subgroup
## # A tibble: 3 × 2
##   Sub_group     n
##      <fctr> <int>
## 1    Benign    34
## 2   Healthy    66
## 3       CRC   100
ggplot(subgroup, aes(x=Sub_group, y=n)) + 
  geom_bar(stat="identity")

2.3 Zooming

We can change the limit of x-axis and y-axis to see the plot details.

h <- ggplot(CRC, aes(SERPINA3))

# Change the bar width
h + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

h + geom_histogram(binwidth = 0.1)

# Change the limit of x-axis and y-axis
h + geom_histogram(binwidth = 0.1) +
  coord_cartesian(xlim = c(12.5, 16))

We can also color specific point in scatter plot to see how it looks like

ggplot(data = CRC) +
  geom_point(mapping = aes(x = SERPINA3, y = TIMP1))

CRC2 <- CRC
highlight.sample <- "P1D2"
CRC2$highlight <- ifelse(CRC2$Sample == highlight.sample, "highlight", "normal")
textdf <- CRC2[CRC2$Sample == highlight.sample, ]
mycolours <- c("highlight" = "red", "normal" = "grey50")

ggplot(data = CRC2, aes(x = SERPINA3, y = TIMP1)) +
  geom_point(size = 3, aes(colour = highlight)) +
  scale_color_manual("Sample", values = mycolours) +
  geom_text(data = textdf, aes(x = SERPINA3, y = TIMP1* 0.99, label = highlight.sample), colour = "red")

2.4 Change labels, themes, and scales

2.4.1 titles, subtitles and captions

p1 <- ggplot(data = CRC) +
  geom_point(mapping = aes(x = SERPINA3, y = TIMP1, color = Sub_group))
p1 + labs(title = "Compare between sub groups",
          subtitle = "Benign samples are mixed with the other two groups",
          caption = "Data vis example")

# Axis labels and legend titles
p1 + labs(x = "Protein SERPINA3", y = "Protein TIMP1", color = "Sub groups")

2.4.2 Theme: Change appearance of non-data elements

We can change the plot background by theme. Reference: http://ggplot2.tidyverse.org/reference/theme.html

p1 + theme_grey()

p1 + theme_classic()

p1 + theme_dark()

p1 + theme_light()

p1 + theme_void()

p1 + theme(panel.background = element_rect(fill = "white", colour = "grey50"))

We can further change the appearance and the orientation angle of title and axis labels

CRC.two.prot <- CRC[,c("SERPINA3","TIMP1","Sample")]
plot.data <- CRC.two.prot[1:20,] %>% gather(Protein, Abundance, -Sample)

p2 <- ggplot(plot.data) + 
  geom_line(aes(x=Sample, y = Abundance, group = Protein, colour=Protein))
 
p2 + theme(plot.title = element_text(size=20, colour="darkblue"),
          axis.text.x = element_text(face="bold", color="blue", size=10, angle=45),
          axis.title.x = element_text(face="bold", colour="#990000", size=20),
          axis.text.y = element_text(face="bold", color="blue", size=14),
          axis.title.y = element_text(face="bold", colour="#990000", size=20))

# Hide x an y axis tick mark labels
p2 + theme(
  axis.text.x = element_blank(),
  axis.title.x = element_blank(),
  axis.text.y = element_blank(),
  axis.title.y = element_blank())

# Remove axis ticks and tick mark labels
p2 + theme(
  axis.ticks = element_blank())

Adjust Legend position

p2 + theme(legend.position = "right") # the default

p2 + theme(legend.position = "bottom")

p2 + theme(legend.position = "none")

Facetting creates tables of graphics by splitting the data into subsets and displaying the same graph for each subset

p3 <- ggplot(CRC) + 
  geom_point(aes(x = SERPINA3, y = TIMP1)) +
  facet_grid(~ Group)
p3

p3 + theme(strip.background = element_rect(colour = "white", fill = "yellow"))

2.4.3 Scales: control the appearance of data elements.

A scale function exists for each aesthetic. We can change color themes

p1 + scale_color_grey()

p1 + scale_color_brewer(palette = "Set1")

We can also change the axis scales

p1 + scale_x_log10() 

p1 + scale_y_log10()

p1 + scale_y_reverse()

Change the limit of x-axis

p1 + scale_x_continuous(name="Protein SERPINA3", limits=c(12, 16)) +
  scale_y_continuous(name="Protein TIMP1")
## Warning: Removed 15 rows containing missing values (geom_point).